Applications of Advanced Data Analytics to Medicine

There are many applications of advanced data analytics to medicine. The health care industry is now awash in data in a way that it has never been before: from clinical data and health outcomes data contained in ever more prevalent electronic health records (EHRs) and longitudinal drug and medical claims to biological data such as gene expression, next-generation DNA sequence data, proteomics, and metabolomics.

One of the most successful application areas

Medicine has been one of the most successful application areas. These are the top 5:

1. Identification of diseases, including in early stage, from electronic health records.
   For example, for a patient with sepsis — which kills more Americans every year than AIDS and breast and prostate cancer combined — hours can make the difference between life and death. A new computer-based method developed at John Hopkins Medicine correctly predicts septic shock in 85 percent of cases, without increasing the false positive rate from screening methods that are common now. More than two-thirds of the time, the method was able to predict septic shock before any organ dysfunction. That is a 60 percent improvement over existing screening protocols.
2. Identification of proper treatments, avoiding unnecessary treatments.
   For example, in its September 2013 report “Electronic Health Records Linked to Improved Care for Patients With Diabetes”, Kaiser Permanente found out that “The use of electronic health records in clinical settings was associated with a decrease in emergency room visits and hospitalizations for patients with diabetes, according to a study published in the Journal of the American Medical Association.
3. Identification of lesions in medical images of different types (X-rays, MRI, PET).
   For example, Quekel et al (see here) found that lung cancer was missed in one-fifth of cases, even though in retrospect the lesions were entirely visible! In nearly half of these cases, cancers had again been missed at least twice on subsequent X-rays. Advanced data analytics can greatly help to increase the accuracy, speed and efficiency of medical image analysis.
4. Personalized medicine
   Data analytic experts can help doctors customize treatment for each patient by connecting and analyzing huge databases of clinical information, plus new data sources such as DNA sequences, methylation analyses, RNA expression levels, protein structures, and high-tech images.
5. Identification and forecast of medical costs
   Cost prediction of individual patients, thus allowing for better healthcare management and policy making.
   For example, at four of the hospitals which make up the Assistance Publique-Hôpitaux de Paris (AP-HP), data from internal and external sources — including 10 years’ worth of hospital admissions records has been crunched to come up with day and hour-level predictions of the number of patients expected through the doors (details here).
   As another example, to compare the ability of standard versus enhanced models to predict future high-cost patients, especially those who move from a lower to the upper decile of per capita healthcare expenditures within 1 year — that is, ‘cost bloomers’. The best enhanced model achieved a 21% and 30% improvement in cost capture over a standard diagnosis-based model for predicting population-level high-cost patients and cost bloomers, respectively.